Results 61 to 70 of about 2,135 (206)

Non-intrusive load monitoring and its challenges in a NILM system framework

International Journal of High Performance Computing and Networking, 2019
With the increasing of energy demand and electricity price, researchers gain more and more interest among the residential load monitoring. In order to feed back the individual appliance’s energy consumption instead of the whole-house energy consumption, non-intrusive load monitoring (NILM) is a good choice for residents to respond the time-of-use price
Liu, Xiaodong, Liu, Qi, Lu, Min, Linge, Nigel   +3 more
openaire   +1 more source

Technology and Application of Multi‐Energy System: An Engineering Study in China

Energy Internet, Volume 2, Issue 2, Page 126-137, July 2025.
ABSTRACT Multi‐energy system (MES) is crucial for the development of smart cities. This paper summarises the research progress and achievements from an engineering case study in China which aims at enhancing MES energy efficiency. Key theories and technologies were tested at an MES demonstration site in Hunan, China, using both software and hardware ...
Yong Li, Jinting Li, Yixiu Guo, Yahui Wang, Xusheng Yang, Yong Xu   +5 more
wiley   +1 more source

Electricity usage profile disaggregation of hourly smart meter data [PDF]

, 2018
This paper is motivated by the growing demand of disaggregating electricity consumption measured by smart meters, down to appliance level. The very low 15-min to 60- min granularity of energy measurements available for analysis, as is standard by the ...
Stankovic, Lina, Stankovic, Vladimir, Zhao, Bochao   +2 more
core   +2 more sources

Advances in non‐intrusive type I load monitoring using R‐statistic steady‐state detection and subtractive clustering

IET Smart Grid, Volume 8, Issue 1, January/December 2025.
This paper introduces a novel method that uses advanced clustering techniques based on power and time features to identify Type 1 electrical load profiles within aggregated power measurements. Simulation and experimental results demonstrate the effectiveness of the proposed method.
Luigi Pio Savastio, Elia Brescia, Enrico Elio De Tuglie, Massimo Tipaldi, Giuseppe Leonardo Cascella, Michele Surico, Giovanni Conte, Andrea Polichetti   +7 more
wiley   +1 more source

Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets

, 2014
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Batra, Nipun, Dutta, Haimonti, Kelly, Jack, Knottenbelt, William, Parson, Oliver, Rogers, Alex, Singh, Amarjeet, Srivastava, Mani   +7 more
core   +1 more source

Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection

, 2021
Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances.
Wang, Haijin, Si, Caomingzhe, Zhao, Junhua, Liu, Guolong, Wen, Fushuan   +4 more
openaire   +2 more sources

Generating peak‐aware pseudo‐measurements for low‐voltage feeders using metadata of distribution system operators

IET Smart Grid, Volume 8, Issue 1, January/December 2025.
Distribution system operators (DSOs) face challenges in managing low‐voltage (LV) grids that often lack measurement devices. In order to address this, the authors propose using regression models to estimate pseudo‐measurements based on feeder metadata, weather, and time‐related data.
Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer   +6 more
wiley   +1 more source

A Cost‐Effective NILM Solution With Three‐Point Labelling and Non‐Causal Convolution Technique

IET Smart Grid, Volume 8, Issue 1, January/December 2025.
This work introduces a low‐cost semi‐automatic labelling framework, significantly reducing the barriers to large‐scale NILM applications. Furthermore, a novel combined coordinate and confidence loss function is proposed, targeting key issues in three‐point regression scenarios and enhancing model precision.
Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo   +6 more
wiley   +1 more source

Active Privacy‐Preserving, Distributed Edge–Cloud Orchestration–Empowered Smart Residential Mains Energy Disaggregation in Horizontal Federated Learning

International Transactions on Electrical Energy Systems, Volume 2025, Issue 1, 2025.
Combinations of technical advances in artificial intelligence of things (AIoT) are becoming increasingly fundamental constituents of smart houses, buildings, and factories in cities. In smart grids that ensure the resilient delivery of electrical energy to support cities, effective demand‐side management (DSM) can alleviate ever‐increasing electricity ...
Yu-Hsiu Lin, Yung-Yao Chen, Shih-Hao Wei, Murthy Cherukuri   +3 more
wiley   +1 more source

On performance evaluation and machine learning approaches in non-intrusive load monitoring

Energy Informatics, 2018
Non-Intrusive Load Monitoring (NILM) is a set of techniques to gain deep insights into workflows inside buildings based on data provided by smart meters.
Christoph Klemenjak
doaj   +1 more source